Objective To evaluate effects of remote monitoring of adjuvant chemotherapy related side effects via the Advanced Symptom Management System (ASyMS) on symptom burden, quality of life, supportive care needs, anxiety, self-efficacy, and work limitations. Design Multicentre, repeated measures, parallel group, evaluator masked, stratified randomised controlled trial. Setting Twelve cancer centres in Austria, Greece, Norway, Republic of Ireland, and UK. Participants 829 patients with non-metastatic breast cancer, colorectal cancer, Hodgkin’s disease, or non-Hodgkin’s lymphoma receiving first line adjuvant chemotherapy or chemotherapy for the first time in five years. Intervention Patients were randomised to ASyMS (intervention; n=415) or standard care (control; n=414) over six cycles of chemotherapy. Main outcome measures The primary outcome was symptom burden (Memorial Symptom Assessment Scale; MSAS). Secondary outcomes were health related quality of life (Functional Assessment of Cancer Therapy—General; FACT-G), Supportive Care Needs Survey Short-Form (SCNS-SF34), State-Trait Anxiety Inventory—Revised (STAI-R), Communication and Attitudinal Self-Efficacy scale for cancer (CASE-Cancer), and work limitations questionnaire (WLQ). Results For the intervention group, symptom burden remained at pre-chemotherapy treatment levels, whereas controls reported an increase from cycle 1 onwards (least squares absolute mean difference −0.15, 95% confidence interval −0.19 to −0.12; P<0.001; Cohen’s D effect size=0.5). Analysis of MSAS sub-domains indicated significant reductions in favour of ASyMS for global distress index (−0.21, −0.27 to −0.16; P<0.001), psychological symptoms (−0.16, −0.23 to −0.10; P<0.001), and physical symptoms (−0.21, −0.26 to −0.17; P<0.001). FACT-G scores were higher in the intervention group across all cycles (mean difference 4.06, 95% confidence interval 2.65 to 5.46; P<0.001), whereas mean scores for STAI-R trait (−1.15, −1.90 to −0.41; P=0.003) and STAI-R state anxiety (−1.13, −2.06 to −0.20; P=0.02) were lower. CASE-Cancer scores were higher in the intervention group (mean difference 0.81, 0.19 to 1.43; P=0.01), and most SCNS-SF34 domains were lower, including sexuality needs (−1.56, −3.11 to −0.01; P<0.05), patient care and support needs (−1.74, −3.31 to −0.16; P=0.03), and physical and daily living needs (−2.8, −5.0 to −0.6; P=0.01). Other SCNS-SF34 domains and WLQ were not significantly different. Safety of ASyMS was satisfactory. Neutropenic events were higher in the intervention group. Conclusions Significant reduction in symptom burden supports the use of ASyMS for remote symptom monitoring in cancer care. A “medium” Cohen’s effect size of 0.5 showed a sizable, positive clinical effect of ASyMS on patients’ symptom experiences. Remote monitoring systems will be vital for future services, particularly with blended models of care delivery arising from the covid-19 pandemic. Trial registration Clinicaltrials.gov NCT02356081 .

Open and honest communication between families is integral to caring for patients with progressive terminal illnesses. Talking to children about death, dying and bereavement, however, has always been a taboo subject. The specialist palliative care team in Gateshead NHS Foundation Trust share how they succesfully collaborated with a local secondary school to encourage young people to talk about these subjects.

Media fragmentation has diluted the impact of most media. To achieve even average coverage across all customer groups involves buying multiple spots in diverse channels. Conventional media metrics have not helped advertisers to track these elusive consumer groups. Media-neutral planning makes even greater demands on data capabilities.To address this growing complexity and decreasing effectiveness, marketers need to use data characteristics to define their choice of media, rather than profiling the consumers of media that have already been planned. In this way, a better fit can be achieved between the target audience and the media it uses. For brands with multiple customer franchises, this may be the only way to increase ROI on their marketing. The problem with averagingIf Lord Leverhulme had possessed a database of Lever Bros customers, he might never have been forced to admit that, although he knew half his advertising budget was wasted, he did not know which half. The answer could have been found by plotting two key variables -the index of media coverage across the total customer base and the relative value of customer deciles. As Figure 1 shows, half of the expenditure on media has gone towards achieving coverage of customer groups that are essentially unprofitable.The way in which media has historically been planned and bought by advertisers -or packaged and sold by media owners -has been based on the mass-market model. Since the audiences claimed by media owners were broad, the coverage sold to advertisers was averaged out, either to avoid having unsold inventory in unpopular spaces or through the simple regression-to-mean effect of having a mass audience. By delivering an overall average of exposure across the population, money is effectively being left on the table. As this model also identifies, there is a significant gap in the level of exposure of the most valuable customer groups to the media being used.

BACKGROUND Interest in the application of predictive risk models (PRMs) in healthcare to identify people most likely to experience disease and treatment-related complications is increasing. In cancer care, these techniques are focused primarily on prediction of survival or life-threatening toxicities (e.g. febrile neutropenia). Fewer studies focused on use of PRMs for symptoms or supportive care needs. The application of PRMs to chemotherapy-related symptoms (CRS) would enable earlier identification and initiation of prompt, personalised and tailored interventions. While some PRMs exist for CRS, few were translated into clinical practice and human factors associated with their use were not reported. OBJECTIVE Explore patients’ and clinicians’ perspectives of the utility and real-world application of PRMs to improve the management of CRS. METHODS Focus groups (n=10) and interviews (n=5) were conducted with patients (n=28) and clinicians (n=26) across five European countries. Interactions were audio-recorded, transcribed verbatim and analysed thematically. RESULTS Both clinicians and patients recognized the value of having individualised risk predictions for CRS and appreciated how this type of information would facilitate the provision of tailored preventative treatments and/or supportive care interactions. However cautious and skeptical attitudes towards the use of PRMs in clinical care were noted by both groups particularly in relationship to the uncertainty regarding how the information would be generated. Visualisation and presentation of PRM information in a usable and useful format for both patients and clinicians was identified as a challenge to their successful implementation in clinical care. CONCLUSIONS Findings from this study provide information on clinicians’ and patients’ perspectives on the clinical use of PRMs for the management of CRS. These international perspectives are important because they provide insight into the risks and benefits of using PRMs to evaluate CRS. In addition, they highlight the need to find ways to more effectively present and use this information in clinical practice. Further research that explores the best ways to incorporate this type of information while maintaining the human side of care is warranted. CLINICALTRIAL This paper reports on a secondary objective from a larger programme of work Trial Registration: Clinical Trials.gov NCT02356081

Background There is wide inequity in specialist palliative care provision across settings. The absence of any standard way to group by case complexity is a barrier to addressing these inequities. Aim We therefore aimed to develop a casemix classification for UK specialist palliative care across settings, by identifying/ grouping patient-level attributes at the start of an episode of care that predict costs of care provision within that episode. Design Cohort study with prospective collection of patient demographic and clinical variables, potential complexity and casemix criteria, and patient-level resource use. Results 2,469 participants were recruited (mean age 71.6, 51% male, 75% with cancer), receiving 2,968 episodes of care, from 14 specialist palliative organisations across England. Episodes of care lasted: median (range) 8 days (1-402) in hospital advisory palliative care, 12 days (1-140) in inpatient palliative units, 30 days (1-313) in community palliative care. Median cost per day (interquartile range) were: £56 (£31-100) in hospital advisory, £365 (£176-£698) within inpatient, and £21 (£6-£49) in community care. Seven hospital advisory, six inpatient, six community casemix classes for specialist palliative care, based on seven casemix variables (pain, other physical symptoms, psychological symptoms, functional status, palliative Phase of Illness, living alone, and family distress) predict per-diem costs. Conclusion The casemix classes show cost weight variations by up to 60% (in hospital advisory palliative care), up to 4.5fold (in inpatient hospices), and approaching 3-fold (in community palliative care). The proposed casemix classification helps to understand these variations systematically and at scale; for practice, policy (including funding), and research, to help address inequities and provide fair, equitable and transparent palliative care to all who need it.